In general, high-density solid-state detector technology, coupled with affordable, terabyte-scale data storage, has greatly facilitated the development of high-throughput imaging instruments. In the biological realm, high-throughput digital imaging has undergone a period of exponential growth catalyzed by changes in imaging hardware and the need of big-data-driven analysis. In particular, high-throughput whole slide imaging (“WSI”) systems have found important life-science applications, including molecular profiling, live-cell assays, drug discovery, large-scale brain mapping, rare cell screening, RNA interference studies, etc.
In the medical realm, there has been an upsurge in worldwide attention on digital pathology, which converts tissue sections into digital slides that can be viewed, managed, and analyzed on computer screens. Converting microscope slides into digital images also enable teleconsultations and adoption of artificial intelligence technologies for disease diagnosis. This field represents an emerging market.
A technical barrier for high-throughput WSI instruments is the time-consuming autofocusing process, which imposes a limit on the achievable throughput and may cause photobleaching damages to the samples. Autofocusing issues have often been cited as the culprit for poor image quality in digital pathology, in particular for tissue sections with low image contrast. This is generally not because autofocusing is difficult to do, but rather because of the typical need to perform accurate autofocusing at high speed.
There are two types of completing autofocusing methods in current high-throughput imaging systems: 1) laser reflection based methods and 2) image contrast based methods.
For laser-reflection based methods, an infrared laser beam is reflected by the glass surface and creates a reference point to determine the distance between the glass surface and the objective lens, as shown FIG. 1A. This method works well for a sample that has a fixed distance off the glass surface. However, this method can fail if a sample varies its location from the surface, such as with tissue slides. This may be because the focus is maintained at a constant distance above the glass surface, and thus, it cannot track the sample topography variations above the surface.
Different from the laser-reflection method, the image-contrast-based methods generally track topographic variations and identify the optimal focal position through image processing, as shown in FIG. 1B.
This method acquires multiple images by moving the sample along the z direction and attempts to calculate the optimal focal position by maximizing a figure of merit of the acquired images (such as image contrast, entropy, and frequency content). Evident by its name, this method generally relies on the contrast of the sample and it can be difficult to handle transparent or low-contrast samples.
Since z-stacking increases the total scanning time, image-contrast-based methods can achieve improved imaging performance by trading off system throughput. It is noted that focusing on every tile can be time-consuming. To attempt to alleviate the time burden, some conventional WSI systems create a “focus map” prior to scanning, or survey focus points every n tiles in effect skipping areas to sacrifice the focusing accuracy. Each focal point is then triangulated to re-create a theoretical map of the topographic surface of the sample.
In some current automated imaging systems, the laser-based method is mainly used in surface inspection and some life-science applications, where the samples have a fixed distance off the reference surface.
The image-contrast-based method, on the other hand, can track the topographic variation of the sample above the glass surface and can be employed in some commercial WSI systems. Despite its application in current WSI systems, it is unclear whether the image-contrast-based method can be used for high-throughput fluorescence imaging, where samples are typically transparent under the bright-field illumination. If one uses the brightfield channel for autofocusing, no substantial image contrast will be generated in the captured images. If one uses the fluorescence channel for autofocusing, it can be time-consuming to capture multiple fluorescence images under the low-light condition. The sample may also be damaged due to the photo-bleaching effect.
A need exists among end-users and/or manufacturers to develop microscopy/imaging assemblies that include improved features/structures. In addition, a need remains for instruments, assemblies and methods that allow imaging techniques (e.g., microscopic imaging techniques) through designs and techniques that are easily understood and implemented.
Thus, an interest exists for improved microscopy/imaging assemblies and related methods of use. These and other inefficiencies and opportunities for improvement are addressed and/or overcome by the assemblies, systems and methods of the present disclosure.